A context-aware approach for vessels’ trajectory prediction

An accurate vessel trajectory prediction can improve safety management at sea. Although the vessels' trajectories can be affected by complex and high dimensional context factors such as wind, wave size, and current, the research in incorporating them in trajectory prediction is still burgeoning. This paper proposes a context-aware data-driven framework for vessel trajectory prediction. In this regard, trajectories are enriched with context information at the first stage. This includes evaluating methods of trajectory annotation. Next, feature selection techniques are applied to solve the problems caused by high dimensionality, i.e., incensement in the complexity and the computational load. Finally, selected factors are used in a Context-aware Long Short-Term Memory (CLSTM) network. The implementations on the Automatic Identification System (AIS), weather, and ocean condition dataset around the English Channel collected during February 2016, indicated that the spline interpolation is suitable for trajectory annotation. Water depth, wave direction, and height are the most influencing context in vessels’ data-driven movement prediction. The predictions made by the proposed framework are 15.31% more accurate than the prediction of a Long Short-Term Memory (LSTM) network. Therefore, the proposed context-aware trajectory prediction framework might suit maritime systems and applications like collision avoidance, vessel route planning, and anomaly detection.

• Record URL:
  • https://doi.org/10.1016/j.oceaneng.2023.114916
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0029801823013008
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Mehri, Saeed
  • 0000-0002-4423-308X
  • Alesheikh, Ali
  • 0000-0001-9537-9401
  • Basiri, Anahid
• Publication Date: 2023-8-15

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 114916
• Serial:
  • Ocean Engineering
  • Volume: 282
  • Issue Number: 0
  • Publisher: Pergamon
  • ISSN: 0029-8018
  • EISSN: 1873-5258
  • Serial URL: http://www.sciencedirect.com/science/journal/00298018

Subject/Index Terms

• TRT Terms: Alertness; Data analysis; Machine learning; Predictive models; Ships; Vehicle trajectories
• Geographic Terms: English Channel
• Subject Areas: Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01891860
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 29 2023 4:46PM